The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …

Abstract Video-Text Pre-training (VTP) aims to learn transferable representations for various
downstream tasks from large-scale web videos. To date, almost all existing VTP methods …

Text-Video Retrieval (TVR) aims to align relevant video content with natural language
queries. To date, most state-of-the-art TVR methods learn image-to-video transfer learning …

Significant progress has been made in video captioning in recent years. However, most
existing methods directly learn from all given captions without distinguishing the styles of …

Text-Video Retrieval (TVR) aims to align and associate relevant video content with
corresponding natural language queries. Most existing TVR methods are based on large …

Video grounding aims to locate a moment of interest matching the given query sentence
from an untrimmed video. Previous works ignore the\emph {sparsity dilemma} in video …

Accurate evaluation of financial question answering (QA) systems necessitates a
comprehensive dataset encompassing diverse question types and contexts. However …

While vision-language pre-trained models (VL-PTMs) have advanced multimodal research
in recent years, their mastery in a few languages like English restricts their applicability in …

Recent advancements in video-based large language models (Video LLMs) have witnessed
the emergence of diverse capabilities to reason and interpret dynamic visual content …

Zero-shot temporal action detection (ZS-TAD), aiming to recognize and detect new and
unseen video actions, is an emerging and challenging task with limited solutions. Recent …